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1. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

Author

Mark I. McCarthy, Weihua Meng, Brian L. Yaspan, M Imamura, Mark W. Christiansen, Niina Sandholm, Yii-Der Ida Chen, Sharon G. Adler, Lucia Sobrin, Craig L. Hanis, Valeriya Lyssenko, Shiro Maeda, Yang Hai, Paul Mitchell, Roberta McKean-Cowdin, Xiuqing Guo, John R. Sedor, David S. Siscovick, Sudha K. Iyengar, Heather Hancock, Jane Z. Kuo, Barbara E.K. Klein, David-Alexandre Tregouet, Elisabet Agardh, Kent D. Taylor, Andrew D. Morris, S. Mohsen Hosseini, Andrew D. Paterson, I-Te Lee, Wayne Huey-Herng Sheu, Emma Ahlqvist, Kathryn P. Burdon, Leif Groop, Ayellet V. Segrè, Samy Hadjadj, Samaneh Davoudi, Lynn K. Stanwyck, Emily Y. Chew, Xiaohui Li, Michael A. Grassi, Jie Jin Wang, Samuela Pollack, Albert V. Smith, Kyu Hyung Park, Michiaki Kubo, Mary Frances Cotch, Yucheng Jia, Ching J. Chen, Colin N. A. Palmer, Helen M. Colhoun, Alan D. Penman, R. Varma, Per-Henrik Groop, Tien Yin Wong, Barry I. Freedman, Eli Ipp, Alex S. F. Doney, Gavin Tan, Ronald Klein, Kaanan P. Shah, Jamie E Craig, Donald W. Bowden, Jerome I. Rotter, Robert P. Igo, Darryl Nousome, Ching-Yu Cheng, Michel Marre, Maggie C.Y. Ng, Latchezar Dimitrov, Jeeyun Ahn, Atsushi Takahashi, Richard A. Jensen, Aaron Leong, Jihye Kim, Iiro Toppila, Elizabeth J. Rossin, Alkes L. Price, Diabetes and Obesity Research Program, University of Helsinki, Department of Medicine, Nefrologian yksikkö, Research Programs Unit, Clinicum, HUS Abdominal Center, and Per Henrik Groop / Principal Investigator

Subjects
Blood Glucose, 0301 basic medicine, Oncology, medicine.medical_specialty, Genotype, Endocrinology, Diabetes and Metabolism, LOCI, 030209 endocrinology & metabolism, Genome-wide association study, VARIANTS, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Meta-Analysis as Topic, RESOURCE, Internal medicine, Diabetes mellitus, REVEALS, Genetic variation, Internal Medicine, medicine, Humans, Genetic Predisposition to Disease, CHINESE PATIENTS, Allele, METAANALYSIS, POLYMORPHISMS, Glycemic, Glycated Hemoglobin, Diabetic Retinopathy, business.industry, Diabetic retinopathy, medicine.disease, PREVALENCE, 3. Good health, SEVERITY, 030104 developmental biology, Diabetes Mellitus, Type 2, 3121 General medicine, internal medicine and other clinical medicine, Multiple comparisons problem, RISK-FACTORS, Medical genetics, Erratum, business, Genome-Wide Association Study, Protein Binding
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2018

2. Erratum. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019;68:441—456

Author

Aaron Leong, Yii-Der Ida Chen, Iiro Toppila, Elizabeth J. Rossin, Alan D. Penman, I-Te Lee, Kathryn P. Burdon, Yucheng Jia, Sudha K. Iyengar, Helen M. Colhoun, Michael A. Grassi, Jie Jin Wang, Tien Yin Wong, Barry I. Freedman, Kaanan P. Shah, Ching J. Chen, Weihua Meng, Brian L. Yaspan, Samaneh Davoudi, David S. Siscovick, Per-Henrik Groop, Samy Hadjadj, Michel Marre, Ching-Yu Cheng, John R. Sedor, Xiuqing Guo, Albert V. Smith, Robert P. Igo, Kyu Hyung Park, Jerome I. Rotter, Andrew D. Paterson, Samuela Pollack, Sharon G. Adler, Alkes L. Price, Emily Y. Chew, Emma Ahlqvist, Eli Ipp, Jamie E Craig, Richard A. Jensen, Barbara E.K. Klein, Maggie C.Y. Ng, Leif Groop, Craig L. Hanis, Xiaohui Li, Atsushi Takahashi, Roberta McKean-Cowdin, Mark P. Christiansen, Lynn K. Stanwyck, Paul Mitchell, Shiro Maeda, Alex S. F. Doney, Darryl Nousome, Ronald Klein, Yang Hai, Mark I. McCarthy, Niina Sandholm, Kent D. Taylor, Andrew D. Morris, Rohit Varma, David-Alexandre Trégouët, Jihye Kim, Valeriya Lyssenko, Colin N. A. Palmer, Mary Frances Cotch, Jane Z. Kuo, Elisabet Agardh, S. Mohsen Hosseini, Michiaki Kubo, Jeeyun Ahn, Gavin Tan, Minako Imamura, Lucia Sobrin, Heather Hancock, Wayne Huey-Herng Sheu, Donald W. Bowden, Ayellet V. Segrè, and Latchezar Dimitrov

Subjects
Pediatrics, medicine.medical_specialty, Complications, business.industry, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Diabetic retinopathy, medicine.disease, Diabetes mellitus, Internal Medicine, medicine, Duration (project management), business, Glycemic
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2020

3. Integrative cross tissue analysis of gene expression identifies novel type 2 diabetes genes

Author

Rodrigo Bonazzola, Alvaro N. Barbeira, Graeme I. Bell, Andrew P. Morris, Jason M. Torres, Hae Kyung Im, Heather E. Wheeler, Nancy J. Cox, and Kaanan P. Shah

Subjects
Genetics, 0303 health sciences, Adipose tissue, Genome-wide association study, Type 2 diabetes, Biology, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Diabetes mellitus, Gene expression, Cohort, medicine, Pancreas, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we performed a tissue-specific gene association study in a cohort of over 100K individuals (ncases≈ 26K,ncontrols≈ 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 13 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDRrep< 0.05) in an independent study (ncases≈ 10K,ncontrols≈ 62K). We also found enrichment of significant associations in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later onset type 2 diabetes.

Published
2017

4. Genetic predictors of gene expression associated with risk of bipolar disorder

Author

Kaanan P. Shah, Dan L. Nicolae, Eric R. Gamazon, Heather E. Wheeler, Hae Kyung Im, and Nancy J. Cox

Subjects
Genetics, 0303 health sciences, education.field_of_study, Population, Genome-wide association study, Disease, Biology, medicine.disease, 3. Good health, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene expression, medicine, Bipolar disorder, education, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Bipolar disorder (BD) affects the quality of life of approximately 1% of the population and represents a major public health concern. It is known to be highly heritable but large-scale genome-wide association studies (GWAS) have discovered only a handful of markers associated with the disease. Furthermore, the biological mechanisms underlying these markers need to be elucidated. We recently published a gene-level association test, PrediXcan that integrates transcriptome regulation data to characterize the function of these markers in a tissue specific manner. In this study, we developed prediction models for mRNA levels in 10 brain regions using data from the GTEx project and performed PrediXcan analysis in WTCCC as well as in an independent cohort, GAIN. We replicate the association between predicted expression of PTPRE and BD risk in whole blood and recapitulate the association in brain tissues. PTPRE encodes the protein tyrosine phosphatase, receptor type E, that is known to be involved in RAS signaling and activation of voltage-gated K+ channels. We also found a new genome-wide significant association between lower predicted expression of BBX (bobby sox homolog) in the anterior cingulate cortex region of the brain and increased risk of BD (pWTCCC = 7.02 x 10e-6, pGAIN = 4.68 x 10e-3, pmeta = 1.11 x 10e-7). In sum, we used our mechanistically informed approach, PrediXcan, to identify and replicate two novel genome-wide significant genes using existing GWAS studies.

Published
2016
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5. Correction: Corrigendum: Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Ellen L. Goode, Mariza deAndrade, Julie M. Cunningham, David J. Hunter, Gretchen L. Gierach, Jennifer Stone, Andrew D. Paterson, Paula Smith, Aaron D. Norman, Robert Luben, Aditi Hazra, Kamila Czene, Heang Ping Chan, Nicholas W. Knoblauch, Peter A. Fasching, Mark A. Helvie, Pablo Fernández-Navarro, John L. Hopper, Norman F. Boyd, Nicholas J. Wareham, Judith Brown, Deborah J. Thompson, Jingmei Li, Andrew H. Beck, Per Hall, Ruth J. F. Loos, V. Shane Pankratz, Isabel dos-Santos-Silva, Jajini Verghase, Douglas F. Easton, Christopher J. Scott, Rulla M. Tamimi, Jonine D. Figueroa, Fergus J. Couch, Celine M. Vachon, Laura Baglietto, John A. Heit, Sara Lindström, Julie A. Douglas, Graham G. Giles, Peter Kraft, Kaanan P. Shah, Marina Pollán, Robert A. Vierkant, and Melissa C. Southey

Subjects
Multidisciplinary, Breast cancer, medicine, MAMMOGRAPHIC DENSITY, General Physics and Astronomy, Genome-wide association study, General Chemistry, Computational biology, Biology, Bioinformatics, medicine.disease, General Biochemistry, Genetics and Molecular Biology
Published
2015

6. PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Author

Dan L. Nicolae, Nancy J. Cox, Sahar V. Mozaffari, Hae Kyung Im, Joshua C. Denny, Robert J. Carroll, Eric R. Gamazon, Keston Aquino-Michaels, Heather E. Wheeler, Anne E. Eyler, and Kaanan P. Shah

Subjects
Genetics, 0303 health sciences, Candidate gene, Mechanism (biology), In silico, Genomics, Genome-wide association study, Biology, Phenotype, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Published
2015
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7. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Christopher A. Haiman, Robert Luben, Laurence N. Kolonel, Aditi Hazra, Loic Le Marchand, Peter Kraft, Douglas F. Easton, Christy G. Woolcott, Jennifer Stone, Kavitha Krishnan, Sara Lindström, Katharina Heusinger, Julie A. Douglas, Vessela N. Kristensen, Alison M. Dunning, Christopher G. Scott, Qin Wang, Fergus J. Couch, Giske Ursin, V. Shane Pankratz, Matthew B. Jensen, Laura Baglietto, Heang Ping Chan, Matthias W. Beckmann, Rulla M. Tamimi, Celine M. Vachon, Jacques Simard, Peter A. Fasching, Manjeet K. Bolla, Kamila Czene, Kay-Tee Khaw, John L. Hopper, Judith E. Brown, Grethe I. Grenaker Alnæs, Gertraud Maskarinec, Joe Dennis, Graham G. Giles, Jean Leyland, Jingmei Li, Carmel Apicella, Kaanan P. Shah, Louise Eriksson, Irene L. Andrulis, Melissa C. Southey, Paul D.P. Pharoah, Sebastian M. Jud, Per Hall, Inger T. Gram, Isabel dos Santos Silva, Janet E. Olson, Julia A. Knight, Anne Lise Børresen-Dale, Mark A. Helvie, Kristen S. Purrington, Deborah J. Thompson, Ruth Warren, Kyriaki Michailidou, Nicholas J. Wareham, Paula Smith, Julie M. Cunningham, Thompson, Deborah [0000-0003-1465-5799], Luben, Robert [0000-0002-5088-6343], Khaw, Kay-Tee [0000-0002-8802-2903], Wareham, Nicholas [0000-0003-1422-2993], Wang, Jean [0000-0002-9139-0627], Dennis, Joe [0000-0003-4591-1214], Pharoah, Paul [0000-0001-8494-732X], Dunning, Alison [0000-0001-6651-7166], Easton, Douglas [0000-0003-2444-3247], and Apollo - University of Cambridge Repository

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Genotype, Genome-wide association study, Breast Neoplasms, Polymorphism, Single Nucleotide, Article, Breast cancer, Polymorphism (computer science), Risk Factors, Internal medicine, Genetic variation, Medicine, Humans, Genetic Predisposition to Disease, Polymorphism, Mammary Glands, Human, Aged, Breast Density, Gynecology, business.industry, MAMMOGRAPHIC DENSITY, Single Nucleotide, Heritability, Middle Aged, medicine.disease, Mammary Glands, Disease Susceptibility, Female, business, Body mass index, Human
Abstract

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

Published
2015

8. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Graham G. Giles, Paula Smith, Rulla M. Tamimi, Celine M. Vachon, John A. Heit, Jennifer Stone, Heang Ping Chan, Ruth J. F. Loos, Melissa C. Southey, Julie M. Cunningham, Peter Kraft, Ellen L. Goode, Christopher G. Scott, Andrew H. Beck, Fergus J. Couch, Andrew D. Paterson, Jajini Verghase, Robert Luben, Kaanan P. Shah, Kamila Czene, Aditi Hazra, John L. Hopper, Peter A. Fasching, Robert A. Vierkant, Sara Lindström, Julie A. Douglas, V. Shane Pankratz, Laura Baglietto, Jingmei Li, Marina Pollán, Judith E. Brown, Isabel dos-Santos-Silva, Douglas F. Easton, Aaron D. Norman, Norman F. Boyd, Mark A. Helvie, Deborah J. Thompson, Per Hall, Jonine D. Figueroa, Gretchen L. Gierach, David J. Hunter, Nicholas W. Knoblauch, Pablo Fernández-Navarro, Mariza deAndrade, Nicholas J. Wareham, Instituto de Salud Carlos III, and Unión Europea

Subjects
Oncology, Genetics and Molecular Biology (all), medicine.medical_specialty, General Physics and Astronomy, Genome-wide association study, Breast Neoplasms, Biology, Bioinformatics, Biochemistry, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Physics and Astronomy (all), 0302 clinical medicine, Breast cancer, Polymorphism (computer science), Internal medicine, medicine, Humans, Genetic Predisposition to Disease, Risk factor, Stage (cooking), Polymorphism, Mammary Glands, Human, 030304 developmental biology, Genetic association, Breast Density, 0303 health sciences, Multidisciplinary, Chemistry (all), Case-control study, Case-Control Studies, Female, Genetic Loci, Genome-Wide Association Study, Biochemistry, Genetics and Molecular Biology (all), General Chemistry, Single Nucleotide, Heritability, medicine.disease, Mammary Glands, 3. Good health, Radiography, 030220 oncology & carcinogenesis, Human
Abstract

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P

Published
2014

9. A Method to Prioritize Quantitative Traits and Individuals for Sequencing in Family-Based Studies

Author

Kaanan P. Shah and Julie A. Douglas

Subjects
Male, Heredity, Epidemiology, lcsh:Medicine, Genome-wide association study, medicine.disease_cause, Gene Frequency, Genome Sequencing, lcsh:Science, Exome, Genetics, 0303 health sciences, Multidisciplinary, 030305 genetics & heredity, Statistics, High-Throughput Nucleotide Sequencing, Genomics, Phenotypes, Genetic Epidemiology, Medicine, Female, Research Article, Biology, Quantitative trait locus, 03 medical and health sciences, Quantitative Trait, Heritable, medicine, Humans, Computer Simulation, Family, Allele, Statistical Methods, Nuclear family, 030304 developmental biology, Models, Statistical, Quantitative Traits, Population Biology, Models, Genetic, Complex Traits, lcsh:R, Computational Biology, Old Order Amish, lcsh:Q, Amish, Mathematics, Founder effect, Genome-Wide Association Study
Abstract

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.

Published
2013

10. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Author

Barbara E. Engelhardt, Sarah Kim-Hellmuth, Sarah Urbut, Ashis Saha, Gen Li, Jessica Halow, Panagiotis Papasaikas, Simona Volpi, Alvaro N. Barbeira, Jared L. Nedzel, Meng Wang, Nikolaos I Panousis, Benedict Paten, Lori E. Brigham, Gao Wang, Mary Barcus, Anthony Payne, Xiaoquan Wen, Nicola J. Rinaldi, Hua Tang, Seva Kashin, Casey Martin, Steven Hunter, Yi-Hui Zhou, Kristen Lee, Ian C. McDowell, Jason M. Torres, Alexandra J. Scott, Kane Hadley, Duyen T. Nguyen, Leslie H. Sobin, Jiamao Zheng, Paul Flicek, Casandra A. Trowbridge, Anjené M. Addington, Omer Basha, Brandon L. Pierce, Pejman Mohammadi, Xiao Li, Halit Ongen, Ki Sung Um, John T. Lonsdale, Alexis Battle, Takunda Matose, Kathryn Demanelis, Yuan He, Bernadette Mestichelli, Kasper D. Hansen, Marcus Hunter, Shin Lin, Richard Hasz, Thomas Juettemann, Ellen Karasik, Taru Tukiainen, Lin Chen, Fred A. Wright, Martijn van de Bunt, Meritxell Oliva, Fidencio J. Neri, François Aguet, Todd L. Edwards, Judith B. Zaugg, Michael Snyder, Gary Walters, Yongjin Park, Mary Goldman, Michael Sammeth, Kristin G. Ardlie, A. Roger Little, Reza Sodaei, Stephen J. Trevanion, Kevin S. Smith, Stephen B. Montgomery, Katherine H. Huang, John A. Stamatoyannopoulos, David E. Tabor, Gene Kopen, Melina Claussnitzer, Nancy Roche, Rodrigo Bonazzola, Deborah C. Mash, Helen M. Moore, Anne Ndungu, Lihua Jiang, Konrad J. Karczewski, Marian S. Fernando, Liqun Qi, Princy Parsana, Ping Guan, Eleazar Eskin, Kate R. Rosenbloom, Jason Bridge, Maria M. Tomaszewski, David A. Davis, Joseph Wheeler, Sarah E. Gould, Morgan Diegel, Eric S. Torstenson, Nancy J. Cox, Daniel R. Zerbino, Joshua M. Akey, Kimberly M. Valentino, Brunilda Balliu, Jingchun Zhu, Joe R. Davis, Nicholas Van Wittenberghe, Michael F. Salvatore, Susan E. Koester, Barbara A. Foster, Andrey A. Shabalin, Ira M. Hall, John Vivian, Eun Yong Kang, Nathan S. Abell, Laure Fresard, Lindsay F. Rizzardi, Ayellet V. Segrè, Dan Sheppard, Rajinder Kaul, Jeffrey McLean, Michael Washington, Joanne Chan, Mark Miklos, Benoit Molinie, Christopher Johns, Laura M. Huckins, Monkol Lek, Christopher D. Brown, Laura A. Siminoff, Alisa McDonald, Abhi Rao, Jean Monlong, Jie Quan, Diego Garrido-Martín, Barbara E. Stranger, Donald F. Conrad, Farhan N. Damani, Cédric Howald, Buhm Han, Michael S. Noble, Caroline Linke, Daniel C. Rohrer, Jimmie B. Vaught, Hae Kyung Im, Audra K. Johnson, Rui Zhang, Muhammad G. Kibriya, Matthew Stephens, Chiara Sabatti, Brian Roe, Latarsha J. Carithers, Robert E. Handsaker, Pushpa Hariharan, Boxiang Liu, Manolis Kellis, YoSon Park, Manuel Muñoz-Aguirre, Lei Hou, Matthew T. Maurano, Ariel D. H. Gewirtz, Ruth Barshir, Michael J. Gloudemans, Li Wang, Gireesh K. Bogu, Christopher Lee, Anita H. Undale, Genna Gliner, Sandra Linder, Serghei Mangul, Andrew A. Brown, Tzintzuni Garcia, Eric Haugen, Michael T. Moser, Jessica Lin, Andrew B. Nobel, Richard Sandstrom, Emmanouil T. Dermitzakis, Kieron Taylor, Brian Jo, Rachna Kumar, Laura Barker, Magali Ruffier, Ferran Reverter, Saboor Shad, Jin Billy Li, Eli A. Stahl, Mark I. McCarthy, William F. Leinweber, Gad Getz, Karna Robinson, Xin Li, Esti Yeger-Lotem, Jemma Nelson, Negin Vatanian, Scott P. Dickinson, Colby Chiang, Jeffrey A. Thomas, Kaanan P. Shah, Maximilian Haeussler, Heather M. Traino, Concepcion R. Nierras, Fan Wu, Hualin S. Xi, Yaping Liu, Daniel Bates, Peter Hickey, Eric R. Gamazon, Jennifer A. Doherty, Ellen Gelfand, Jae Hoon Sul, Jeffery P. Struewing, Beryl B. Cummings, W. James Kent, John Palowitch, Brian Craft, Anna M. Smith, Pedro G. Ferreira, Emily K. Tsang, Andrew P. Feinberg, Nicole C. Lockart, Ruiqi Jian, Robert G. Montroy, Dan L. Nicolae, Yungil Kim, Kevin Myer, Christine B. Peterson, Benjamin J. Strober, Andrew D. Skol, Qin Li, Bryan Gillard, Dana R. Valley, Zachary Zappala, Philip A. Branton, Stephane E. Castel, Daniel G. MacArthur, Mark H. Johnson, Olivier Delaneau, Maghboeba Mosavel, Roderic Guigó, Farzana Jasmine, Scott D. Jewell, Shilpi Singh, Tuuli Lappalainen, Farhad Hormozdiari, and Heather E. Wheeler

Subjects
0301 basic medicine, Science, Quantitative Trait Loci, Gene Expression, General Physics and Astronomy, Genome-wide association study, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Genome-wide association studies, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Meta-Analysis as Topic, Genetic variation, Humans, Computer Simulation, lcsh:Science, Gene, Genetics, Multidisciplinary, Models, Genetic, Chromosome Mapping, Genetic Variation, Tissue-Specific Gene Expression, Robustness (evolution), General Chemistry, Phenotype, 030104 developmental biology, Organ Specificity, Trait, lcsh:Q, Data integration, Genome-Wide Association Study
Abstract

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

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